{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLM: Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.6\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import OrderedDict\n",
    "from ipywidgets import interactive, fixed\n",
    "\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "print('Running on PyMC3 v{}'.format(pm.__version__))\n",
    "rndst = np.random.RandomState(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fairly minimal reproducable example of Model Selection using WAIC, and LOO as currently implemented in PyMC3. \n",
    "\n",
    "This example creates two toy datasets under linear and quadratic models, and then tests the fit of a range of polynomial linear models upon those datasets by using Widely Applicable Information Criterion (WAIC), and leave-one-out (LOO) cross-validation using Pareto-smoothed importance sampling (PSIS). \n",
    "\n",
    "The example was inspired by Jake Vanderplas' [blogpost](https://jakevdp.github.io/blog/2015/08/07/frequentism-and-bayesianism-5-model-selection/) on model selection, although Cross-Validation and Bayes Factor comparison are not implemented. The datasets are tiny and generated within this Notebook. They contain errors in the measured value (y) only.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(n=20, p=0, a=1, b=1, c=0, latent_sigma_y=20):\n",
    "    ''' \n",
    "    Create a toy dataset based on a very simple model that we might\n",
    "    imagine is a noisy physical process:\n",
    "        1. random x values within a range\n",
    "        2. latent error aka inherent noise in y\n",
    "        3. optionally create labelled outliers with larger noise\n",
    "\n",
    "    Model form: y ~ a + bx + cx^2 + e\n",
    "\n",
    "    NOTE: latent_sigma_y is used to create a normally distributed,\n",
    "    'latent error' aka 'inherent noise' in the 'physical' generating\n",
    "    process, rather than experimental measurement error. \n",
    "    Please don't use the returned `latent_error` values in inferential \n",
    "    models, it's returned in the dataframe for interest only.\n",
    "    '''\n",
    "\n",
    "    df = pd.DataFrame({'x': rndst.choice(np.arange(100), n, replace=False)})\n",
    "\n",
    "    # create linear or quadratic model\n",
    "    df['y'] = a + b*(df['x']) + c*(df['x'])**2\n",
    "\n",
    "    # create latent noise and marked outliers\n",
    "    df['latent_error'] = rndst.normal(0, latent_sigma_y, n)\n",
    "    df['outlier_error'] = rndst.normal(0, latent_sigma_y*10, n)\n",
    "    df['outlier'] = rndst.binomial(1, p, n)\n",
    "\n",
    "    # add noise, with extreme noise for marked outliers\n",
    "    df['y'] += ((1-df['outlier']) * df['latent_error'])\n",
    "    df['y'] += (df['outlier'] * df['outlier_error'])\n",
    "\n",
    "    # round\n",
    "    for col in ['y', 'latent_error', 'outlier_error', 'x']:\n",
    "        df[col] = np.round(df[col], 3)\n",
    "\n",
    "    # add label\n",
    "    df['source'] = 'linear' if c == 0 else 'quadratic'\n",
    "\n",
    "    # create simple linspace for plotting true model\n",
    "    plotx = np.linspace(df['x'].min() - np.ptp(df['x'].values)*.1,\n",
    "                        df['x'].max() + np.ptp(df['x'].values)*.1, 100)\n",
    "\n",
    "    ploty = a + b * plotx + c * plotx ** 2\n",
    "    dfp = pd.DataFrame({'x': plotx, 'y': ploty})\n",
    "\n",
    "    return df, dfp\n",
    "\n",
    "\n",
    "def interact_dataset(n=20, p=0, a=-30, b=5, c=0, latent_sigma_y=20):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Interactively generate dataset and plot\n",
    "    '''\n",
    "\n",
    "    df, dfp = generate_data(n, p, a, b, c, latent_sigma_y)\n",
    "\n",
    "    g = sns.FacetGrid(df, height=8, hue='outlier', hue_order=[True, False],\n",
    "                      palette=sns.color_palette('Set1'), legend_out=False)\n",
    "\n",
    "    g.map(plt.errorbar, 'x', 'y', 'latent_error', marker=\"o\",\n",
    "          ms=10, mec='w', mew=2, ls='', elinewidth=0.7).add_legend()\n",
    "\n",
    "    plt.plot(dfp['x'], dfp['y'], '--', alpha=0.8)\n",
    "\n",
    "    plt.subplots_adjust(top=0.92)\n",
    "    g.fig.suptitle('Sketch of Data Generation ({})'.format(\n",
    "        df['source'][0]), fontsize=16)\n",
    "\n",
    "\n",
    "def plot_datasets(df_lin, df_quad, dfp_lin, dfp_quad):\n",
    "    '''\n",
    "    Convenience function:\n",
    "    Plot the two generated datasets in facets with generative model\n",
    "    '''\n",
    "\n",
    "    df = pd.concat((df_lin, df_quad), axis=0)\n",
    "\n",
    "    g = sns.FacetGrid(col='source', hue='source', data=df, height=6,\n",
    "                      sharey=False, legend_out=False)\n",
    "\n",
    "    g.map(plt.scatter, 'x', 'y', alpha=0.7, s=100, lw=2, edgecolor='w')\n",
    "\n",
    "    g.axes[0][0].plot(dfp_lin['x'], dfp_lin['y'], '--', alpha=0.6)\n",
    "    g.axes[0][1].plot(dfp_quad['x'], dfp_quad['y'], '--', alpha=0.6)\n",
    "\n",
    "\n",
    "def plot_traces(traces, retain=1000):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Plot traces with overlaid means and values\n",
    "    '''\n",
    "\n",
    "    ax = pm.traceplot(traces[-retain:],\n",
    "                      lines=tuple([(k, {}, v['mean'])\n",
    "                                   for k, v in pm.summary(traces[-retain:]).iterrows()]))\n",
    "\n",
    "    for i, mn in enumerate(pm.summary(traces[-retain:])['mean']):\n",
    "        ax[i, 0].annotate('{:.2f}'.format(mn), xy=(mn, 0), xycoords='data',\n",
    "                          xytext=(5, 10), textcoords='offset points', rotation=90,\n",
    "                          va='bottom', fontsize='large', color='#AA0022')\n",
    "\n",
    "\n",
    "def create_poly_modelspec(k=1):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Create a polynomial modelspec string for patsy\n",
    "    '''\n",
    "    return ('y ~ 1 + x ' + ' '.join(['+ np.power(x,{})'.format(j)\n",
    "                                     for j in range(2, k+1)])).strip()\n",
    "\n",
    "\n",
    "def run_models(df, upper_order=5):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Fit a range of pymc3 models of increasing polynomial complexity. \n",
    "    Suggest limit to max order 5 since calculation time is exponential.\n",
    "    '''\n",
    "\n",
    "    models, traces = OrderedDict(), OrderedDict()\n",
    "\n",
    "    for k in range(1, upper_order+1):\n",
    "\n",
    "        nm = 'k{}'.format(k)\n",
    "        fml = create_poly_modelspec(k)\n",
    "\n",
    "        with pm.Model() as models[nm]:\n",
    "\n",
    "            print('\\nRunning: {}'.format(nm))\n",
    "            pm.glm.GLM.from_formula(fml, df,\n",
    "                                    priors={'Intercept': \n",
    "                                            pm.Normal.dist(\n",
    "                                            mu=0, sigma=100)},\n",
    "                                    family=pm.glm.families.Normal())\n",
    "\n",
    "            traces[nm] = pm.sample(2000, tune=1000, init='advi+adapt_diag')\n",
    "\n",
    "    return models, traces\n",
    "\n",
    "\n",
    "def plot_posterior_cr(models, traces, rawdata, xlims,\n",
    "                      datamodelnm='linear', modelnm='k1'):\n",
    "    '''\n",
    "    Convenience function:\n",
    "    Plot posterior predictions with credible regions shown as filled areas.\n",
    "    '''\n",
    "\n",
    "    # Get traces and calc posterior prediction for npoints in x\n",
    "    npoints = 100\n",
    "    mdl = models[modelnm]\n",
    "    trc = pm.trace_to_dataframe(traces[modelnm][-1000:])\n",
    "    trc = trc[[str(v) for v in mdl.cont_vars[:-1]]]\n",
    "\n",
    "    ordr = int(modelnm[-1:])\n",
    "    x = np.linspace(xlims[0], xlims[1], npoints).reshape((npoints, 1))\n",
    "    pwrs = np.ones((npoints, ordr+1)) * np.arange(ordr+1)\n",
    "    X = x ** pwrs\n",
    "    cr = np.dot(X, trc.T)\n",
    "\n",
    "    # Calculate credible regions and plot over the datapoints\n",
    "    dfp = pd.DataFrame(np.percentile(cr, [2.5, 25, 50, 75, 97.5], axis=1).T,\n",
    "                       columns=['025', '250', '500', '750', '975'])\n",
    "    dfp['x'] = x\n",
    "\n",
    "    pal = sns.color_palette('Greens')\n",
    "    f, ax1d = plt.subplots(1, 1, figsize=(7, 7))\n",
    "    f.suptitle('Posterior Predictive Fit -- Data: {} -- Model: {}'.format(datamodelnm,\n",
    "                                                                          modelnm), fontsize=16)\n",
    "    plt.subplots_adjust(top=0.95)\n",
    "\n",
    "    ax1d.fill_between(dfp['x'], dfp['025'], dfp['975'], alpha=0.5,\n",
    "                      color=pal[1], label='CR 95%')\n",
    "    ax1d.fill_between(dfp['x'], dfp['250'], dfp['750'], alpha=0.5,\n",
    "                      color=pal[4], label='CR 50%')\n",
    "    ax1d.plot(dfp['x'], dfp['500'], alpha=0.6, color=pal[5], label='Median')\n",
    "\n",
    "    plt.legend()\n",
    "    ax1d.set_xlim(xlims)\n",
    "    sns.regplot(x='x', y='y', data=rawdata, fit_reg=False,\n",
    "                scatter_kws={'alpha': 0.7, 's': 100, 'lw': 2, 'edgecolor': 'w'}, ax=ax1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate toy datasets\n",
    "\n",
    "### Interactively draft data\n",
    "\n",
    "Throughout the rest of the Notebook, we'll use two toy datasets created by a linear and a quadratic model respectively, so that we can better evaluate the fit of the model selection.\n",
    "\n",
    "Right now, lets use an interactive session to play around with the data generation function in this Notebook, and get a feel for the possibilities of data we could generate.\n",
    "\n",
    "\n",
    "$$y_{i} = a + bx_{i} + cx_{i}^{2} + \\epsilon_{i}$$\n",
    "\n",
    "where:  \n",
    "$i \\in n$ datapoints\n",
    "\n",
    "$\\epsilon \\sim \\mathcal{N}(0,latent\\_sigma\\_y)$\n",
    "\n",
    "\n",
    "**NOTE on outliers:** \n",
    "\n",
    "+ We can use value `p` to set the (approximate) proportion of 'outliers' under a bernoulli distribution.\n",
    "+ These outliers have a 10x larger `latent_sigma_y`\n",
    "+ These outliers are labelled in the returned datasets and may be useful for other modelling, see another example Notebook `GLM-robust-with-outlier-detection.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f7c2b0604ab4d6aae55867976e7f8d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='n', options=(5, 50, 5), value=5), Dropdown(description='p', option…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(interact_dataset, n=[5,50,5], p=[0,.5,.05], a=[-50,50],\n",
    "            b=[-10,10], c=[-3,3], latent_sigma_y=[0,1000,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ I've shown the `latent_error` in errorbars, but this is for interest only, since this shows the _inherent noise_ in whatever 'physical process' we imagine created the data.\n",
    "+ There is no _measurement error_.\n",
    "+ Datapoints created as outliers are shown in **red**, again for interest only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create datasets for modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the above interactive plot to get a feel for the effect of the params. Now we'll create 2 fixed datasets to use for the remainder of the Notebook. \n",
    "\n",
    "1. For a start, we'll create a linear model with small noise. Keep it simple.\n",
    "2. Secondly, a quadratic model with small noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 12\n",
    "df_lin, dfp_lin = generate_data(n=n, p=0, a=-30, b=5, c=0, latent_sigma_y=40)\n",
    "df_quad, dfp_quad = generate_data(n=n, p=0, a=-200, b=2, c=3, latent_sigma_y=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatterplot against model line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_datasets(df_lin, df_quad, dfp_lin, dfp_quad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ We now have two datasets `df_lin` and `df_quad` created by a linear model and quadratic model respectively.\n",
    "+ You can see this raw data, the ideal model fit and the effect of the latent noise in the scatterplots above\n",
    "+ In the folowing plots in this Notebook, the linear-generated data will be shown in Blue and the quadratic in Green.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs_lin = df_lin.copy()\n",
    "dfs_lin['x'] = (df_lin['x'] - df_lin['x'].mean()) / df_lin['x'].std()\n",
    "\n",
    "dfs_quad = df_quad.copy()\n",
    "dfs_quad['x'] = (df_quad['x'] - df_quad['x'].mean()) / df_quad['x'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create ranges for later ylim xim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs_lin_xlims = (dfs_lin['x'].min() - np.ptp(dfs_lin['x'].values)/10,\n",
    "                 dfs_lin['x'].max() + np.ptp(dfs_lin['x'].values)/10)\n",
    "\n",
    "dfs_lin_ylims = (dfs_lin['y'].min() - np.ptp(dfs_lin['y'].values)/10,\n",
    "                 dfs_lin['y'].max() + np.ptp(dfs_lin['y'].values)/10)\n",
    "\n",
    "dfs_quad_ylims = (dfs_quad['y'].min() - np.ptp(dfs_quad['y'].values)/10,\n",
    "                  dfs_quad['y'].max() + np.ptp(dfs_quad['y'].values)/10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstrate simple linear model\n",
    "\n",
    "This *linear model* is really simple and conventional, an OLS with L2 constraints (Ridge Regression):\n",
    "\n",
    "$$y = a + bx + \\epsilon$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model using explicit PyMC3 method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sigma_y, b1, b0]\n",
      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:05<00:00, 915.99draws/s] \n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as mdl_ols:  \n",
    "    ## define Normal priors to give Ridge regression\n",
    "    b0 = pm.Normal('b0', mu=0, sigma=100)\n",
    "    b1 = pm.Normal('b1', mu=0, sigma=100)\n",
    " \n",
    "    ## define Linear model\n",
    "    yest = b0 + b1 * df_lin['x']\n",
    "\n",
    "    ## define Normal likelihood with HalfCauchy noise (fat tails, equiv to HalfT 1DoF)\n",
    "    sigma_y = pm.HalfCauchy('sigma_y', beta=10)\n",
    "    likelihood = pm.Normal('likelihood', mu=yest, sigma=sigma_y, observed=df_lin['y'])\n",
    "\n",
    "    traces_ols = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(traces_ols, retain=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ This simple OLS manages to make fairly good guesses on the model parameters - the data has been generated fairly simply after all - but it does appear to have been fooled slightly by the inherent noise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model using PyMC3 GLM method\n",
    "\n",
    "PyMC3 has a module - `glm` - for defining models using a `patsy`-style formula syntax. This seems really useful, especially for defining simple regression models in fewer lines of code. \n",
    "\n",
    "Here's the same OLS model as above, defined using `glm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:04<00:00, 1132.82draws/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as mdl_ols_glm:\n",
    "    # Define priors for intercept and regression coefficients.\n",
    "    priors = {'Intercept': pm.Normal.dist(mu=0., sigma=100.),\n",
    "              'x': pm.Normal.dist(mu=0., sigma=100.),\n",
    "              }\n",
    "    # setup model with Normal likelihood (which uses HalfCauchy for error prior)\n",
    "    pm.glm.GLM.from_formula('y ~ 1 + x', df_lin, family=pm.glm.families.Normal())\n",
    "    \n",
    "    traces_ols_glm = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(traces_ols_glm, retain=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ The output parameters are of course named differently to the custom naming before. Now we have:\n",
    "\n",
    "    `b0 == Intercept`  \n",
    "    `b1 == x`  \n",
    "    `sigma_y == sd`  \n",
    "    \n",
    "    \n",
    "+ However, naming aside, this `glm`-defined model appears to behave in a very similar way, and finds the same parameter values as the conventionally-defined model - any differences are due to the random nature of the sampling.\n",
    "+ We can quite happily use the `glm` syntax for further models below, since it allows us to create a small model factory very easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create higher-order linear models\n",
    "\n",
    "Back to the real purpose of this Notebook, to demonstrate model selection.\n",
    "\n",
    "First, let's create and run a set of polynomial models on each of our toy datasets. By default this is for models of order 1 to 5.\n",
    "\n",
    "### Create and run polynomial models\n",
    "\n",
    "Please see `run_models()` above for details. Generally, we're creating 5 polynomial models and fitting each to the chosen dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 95.185:  12%|█▏        | 23589/200000 [00:11<01:25, 2069.12it/s]   \n",
      "Convergence achieved at 23700\n",
      "Interrupted at 23,699 [11%]: Average Loss = 66,411\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:03<00:00, 1600.45draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 100.25:  12%|█▏        | 23691/200000 [00:13<01:39, 1764.44it/s]   \n",
      "Convergence achieved at 23700\n",
      "Interrupted at 23,699 [11%]: Average Loss = 66,768\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:04<00:00, 1389.61draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 105.36:  12%|█▏        | 23581/200000 [00:14<01:46, 1658.03it/s]   \n",
      "Convergence achieved at 23600\n",
      "Interrupted at 23,599 [11%]: Average Loss = 68,508\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:06<00:00, 860.92draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 109.99:  12%|█▏        | 24092/200000 [00:15<01:50, 1597.36it/s]   \n",
      "Convergence achieved at 24200\n",
      "Interrupted at 24,199 [12%]: Average Loss = 65,362\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:11<00:00, 533.93draws/s]\n",
      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 114.75:  12%|█▏        | 24447/200000 [00:15<01:50, 1584.35it/s]   \n",
      "Convergence achieved at 24500\n",
      "Interrupted at 24,499 [12%]: Average Loss = 64,276\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 5), np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:28<00:00, 208.21draws/s]\n",
      "There were 62 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 188 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6372358436158017, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "models_lin, traces_lin = run_models(dfs_lin, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Average Loss = 1.6448e+06:   7%|▋         | 13881/200000 [00:08<01:50, 1685.36it/s]\n",
      "Convergence achieved at 14000\n",
      "Interrupted at 13,999 [6%]: Average Loss = 5.4105e+08\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:03<00:00, 1700.82draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 193.06:  15%|█▌        | 30693/200000 [00:15<01:26, 1959.84it/s]    \n",
      "Convergence achieved at 30700\n",
      "Interrupted at 30,699 [15%]: Average Loss = 2.2511e+08\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:03<00:00, 1679.86draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 197.5:  15%|█▌        | 30539/200000 [00:19<01:45, 1605.67it/s]     \n",
      "Convergence achieved at 30700\n",
      "Interrupted at 30,699 [15%]: Average Loss = 2.1858e+08\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:04<00:00, 1332.04draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 176.83:  16%|█▌        | 31896/200000 [00:18<01:36, 1740.96it/s]    \n",
      "Convergence achieved at 32000\n",
      "Interrupted at 31,999 [15%]: Average Loss = 2.0112e+08\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:04<00:00, 1234.69draws/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi+adapt_diag...\n",
      "Average Loss = 175.7:  16%|█▌        | 32381/200000 [00:20<01:43, 1614.69it/s]     \n",
      "Convergence achieved at 32500\n",
      "Interrupted at 32,499 [16%]: Average Loss = 2.1393e+08\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, np.power(x, 5), np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 6000/6000 [00:05<00:00, 1145.86draws/s]\n"
     ]
    }
   ],
   "source": [
    "models_quad, traces_quad = run_models(dfs_quad, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A really bad method for model selection: compare likelihoods\n",
    "\n",
    "Evaluate log likelihoods straight from model.logp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfll = pd.DataFrame(index=['k1','k2','k3','k4','k5'], columns=['lin','quad'])\n",
    "dfll.index.name = 'model'\n",
    "\n",
    "for nm in dfll.index:\n",
    "    dfll.loc[nm,'lin'] = - models_lin[nm].logp(pm.summary(traces_lin[nm],\n",
    "                                                          traces_lin[nm].varnames)['mean'].to_dict())\n",
    "    \n",
    "    dfll.loc[nm,'quad'] = - models_quad[nm].logp(pm.summary(traces_quad[nm],\n",
    "                                                            traces_quad[nm].varnames)['mean'].to_dict())\n",
    "\n",
    "dfll = pd.melt(dfll.reset_index(), id_vars=['model'],\n",
    "               var_name='poly', value_name='log_likelihood')\n",
    "dfll.index = pd.MultiIndex.from_frame(dfll[['model', 'poly']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot log-likelihoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFcUALEmSpKIYgCVJklQUA7AkSZKKYgCWJElSUQzAkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRTEAS5IkqSgGYEmSJBXFACxJkqSiGIAlSZJUFAOwJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFcUALEmSpKIYgCVJklSU4boLkCR1X0QcApybmXMjYl9gMdAA7gbmZ+amiFgIHAtsBE7OzNtrK1iSesgZYEkaMBFxKnAlsHO16wLg9Mw8AhgCjouIA4EjgUOAE4BL6qhVkurgDLAkDZ4VwPHAF6rXs4Fl1fYS4D8DCSzNzAbwcEQMR8TMzFzZ82qfw8jItLpL6Cv2o5X9GGMvtl/bATgiPgG8GZgCXEpzcF3MFh+xdaFGSdJvITOvi4hZ43YNVUEXYA2wC/B8YPW4czbv77sAPDq6vu4SAJhZdwEV+9GqH/phL1r1Sz+2pa0lEBExFzgMeA3Nj9D2ZisfsXWpRklSZ8ZPRswARoEnqu0t90vSwGt3DfBRwI+B64EbgBv5zY/Y5nVcnSSpG+6sJi4AjgGWA7cCR0XEpIj4PWBSZq6qq0BJ6qV2l0DsBrwYeCPwEuAfaA6eW37E1pdcI9PKfoyxF63sx8A4BbgiIqYA9wLXZuYzEbEc+C7NyZD5dRYoSb3UbgBeDdyXmU8BGRG/prkMYrO+/iitX9bI9Av7McZetLIfO67MfAg4tNq+n+ZytS3PORM4s5d1SVI/aHcJxHeAoyNiKCL2Ap4H/MtWPmKTJEmS+kpbM8CZeWNEzAFuZ+yjs5+xxUdsXatSkiRJ6pK2b4OWmaduZfdvfMQmSZIk9ROfBCdJkqSiGIAlSZJUFAOwJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFcUALEmSpKIYgCVJklQUA7AkSZKKYgCWJElSUQzAkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRTEAS5IkqSgGYEmSJBXFACxJkqSiGIAlSZJUFAOwJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKMN1FyBJmngR8V7gvdXLnYEDgP8KnA/8vNq/MDOX9bw4SeoxA7AkFSAzFwOLASLiEuBqYDZwamZeV19lktR7LoGQpIJExEHA/pl5Oc0AfGJELI+IRRHhpIikIjjYSVJZFgBnVdv/DHwN+BlwGfCnwMU11fWsRkam1V1CX7EfrezHGHux/QzAklSIiBgBIjNvrnZdnZmj1bGvA2+trbhtGB1dX3cJAMysu4CK/WjVD/2wF636pR/b4hIISSrHHOBfACJiCLgrIl5UHXsdcEddhUlSLxmAJakcAfwUIDMbwPuBr0bEMmAacEWNtUlSz7gEQpIKkZnnbfF6KbC0pnIkqTbOAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRenoQRgRsTvNR2e+HtgILAYawN3A/Mzc1GmBkiRJUje1PQMcETsBnwOerHZdAJyemUcAQ8BxnZcnSZIkdVcnSyDOBy4DHqlezwaWVdtLgHkdXFuSJEmaEG0tgYiI9wIrM/OmiPhEtXsoMxvV9hpgly7UNyFGRqbVXUJfsR9j7EUr+yFJGkTtrgE+EWhExDzgAOAaYPdxx2cAox3WNmFGR9fXXUJfsR9j7EUr+yFJGkRtLYHIzDmZeWRmzgV+BLwbWBIRc6tTjgGWd6VCSZIkqYs6ugvEFk4BroiIKcC9wLVdvLYkSZLUFR0H4GoWeLMjO72eJEmSNJF8EIYkSZKKYgCWJElSUQzAkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRTEAS5IkqSgGYEmSJBXFACxJkqSiGIAlSZJUFAOwJEmSimIAliRJUlGG6y5AkjTxIuKHwBPVy58BnwMuBDYCSzPzrLpqk6ReMwBL0oCLiJ2BocycO27fj4C3Aj8F/jEiXp2Zd9ZUoiT1lAFYkgbfq4BpEbGU5rh/JjA1M1cARMRNwDzAACypCAZgSRp864HzgSuBlwFLgNFxx9cA+9RQ13YZGZlWdwl9xX60sh9j7MX2MwBL0uC7H3gwMxvA/RHxOLDruOMzaA3EfWV0dH3dJQAws+4CKvajVT/0w1606pd+bIt3gZCkwXcisAggIvYCpgHrIuKlETEEHAUsr7E+SeopZ4AlafBdBSyOiO8ADZqBeBPwRWAyzbtAfK/G+iSppwzAkjTgMvMp4L9t5dChva5FkvqBSyAkSZJUFAOwJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFcUALEmSpKIYgCVJklQUA7AkSZKKYgCWJElSUQzAkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRRlu500RsRNwNTALmAr8NfATYDHQAO4G5mfmpq5UKUmSJHVJuzPA7wRWZ+YRwNHAxcAFwOnVviHguO6UKEmSJHVPuwH4K8AZ1fYQsBGYDSyr9i0B5nVWmiRJktR9bS2ByMy1ABExA7gWOB04PzMb1SlrgF26UuEEGBmZVncJfcV+jLEXreyHJGkQtRWAASJib+B64NLM/FJE/O24wzOA0U6Lmyijo+vrLqGv2I8x9qKV/ZAkDaK2lkBExB7AUuBjmXl1tfvOiJhbbR8DLO+8PEmSJKm72p0BXgC8ADgjIjavBf4w8JmImALcS3NphCRJktRX2l0D/GGagXdLR3ZWjiRJkjSxfBCGJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFaXtJ8FJknYMEbETcDUwC5gK/DXwc+BG4IHqtM9m5pdrKVCSeswALEmD753A6sx8V0TsCvwIOBu4IDMX1VuaJPWeAViSBt9XGHs65xCwEZgNREQcR3MW+OTMXFNTfds0MjKt7hL6iv1oZT/G2IvtZwCWpAGXmWsBImIGzSB8Os2lEFdm5h0RcRqwEPhIfVU+u9HR9XWXAMDMuguo2I9W/dAPe9GqX/qxLf4SnCQVICL2Bm4GvpCZXwKuz8w7qsPXA6+urThJ6jEDsCQNuIjYA1gKfCwzr6523xQRf1Btvw64Y6tvlqQB5BIISRp8C4AXAGdExBnVvr8E/i4ingZ+BZxUV3GS1GsGYEkacJn5YeDDWzn0ml7XIkn9wCUQkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRTEAS5IkqSgGYEmSJBXFACxJkqSiGIAlSZJUFAOwJEmSimIAliRJUlEMwJIkSSqKAViSJElFMQBLkiSpKAZgSZIkFcUALEmSpKIYgCVJklQUA7AkSZKKYgCWJElSUQzAkiRJKooBWJIkSUUxAEuSJKkoBmBJkiQVxQAsSZKkohiAJUmSVBQDsCRJkopiAJYkSVJRDMCSJEkqigFYkiRJRRnu5sUiYhJwKfAqYAPw/sx8sJtfQ5LUHY7ZkkrV7RngtwA7Z+YfAh8HFnX5+pKk7nHMllSkbgfgw4F/AsjM24CDunx9SVL3OGZLKtJQo9Ho2sUi4krgusxcUr1+GNgnMzd27YtIkrrCMVtSqbo9A/wEMGP89R1IJalvOWZLKlK3A/CtwBsAIuJQ4Mddvr4kqXscsyUVqat3gQCuB14fEf8HGAL+e5evL0nqHsdsSUXq6hpgSZIkqd/5IIwBEBFT666hH0TE79iLpojYve4a+kVETIqIF1b3vJVq5zjV5JjdynG7qVdjtjPAO5CIeBNwMfA0cFpmfrna/63MfG2txdUgIl4BfBJ4DPgicCXwDPDhzLyxztp6LSL222LXNcC7ATLz/t5XVK+IuCoz3xcRh9D82VhN85e9Tqxu9yVNOMfsVo7ZrRy3x9QxZnd7DXBXRcTNwJb/OxwCGpl5WA0l1e004ACaM/dfiYidM/PzNHtSosuAM4BZwLXAfsCvgSVAaYPpN4H1wCM0fx4C+BzQAIr7hxZ4SfXn3wDHZOYDEbEX8L+BI+sra/A5brdwzG7lmN3KcXtMz8fsvg7ANJ9MdAXwXwBvzQNPZeZjABFxHPCt6r6dpU7jT8rMZcCyiPijzHwUICJK/Fk5iOY/Lp/NzH+OiJsz84/qLqoPPJOZDwBk5iMug+gJx+0xjtmtHLNbOW7/pp6N2X2/BCIiPgo8mJnX111L3SLiGmAVcEZmrouIvYGbgJHM3Kve6novIq6i+Q/JSZm5qdr3ceCAzDyh1uJqEBHDwPnAo8DrSx5II+KOanMazZ58keZjfnfJzHfWVlghHLebHLNbOWb/JsftpjrG7L6fDcnM84DRza+rRfOX1VhSnU4E7qL6aCQzfw4cTZkfHQF8ALiB1o9HHgEer6ecelUPMPgH4P8Bk0r+u5KZs4HDgP8F3AZsAu6h+XGrJpjj9n9wzG7lmL0Fx+2mOsbsvg/Alb+KiIOqG7V/H/hZ3QXVITM3ZuZi4GPj+vENYEW9ldUjMzdl5tdp/fk4FfhpzaXV6WzgJ8DHKPjvCkBmbgDeTHNG4UDgfwAP1FpUWYoftx2zWzlmPyvHbXo/Zvf7GuDN3kLzf0hTgLdn5r0111M3+9HKfozZ3IudsBfgz0ad7P0Ye9HKfrRy3B7Ts5+Nvl4DHBGfYuyXBfak+dHRYoDMXFBTWbWxH63sxxh70cp+1Mfej7EXrexHK/sxpo5e9PsM8H3jthNYVlchfcJ+tLIfY+xFK/tRH3s/xl60sh+t7MeYnveir2eAJUmSpG7bUX4JTpIkSeoKA7AkSZKKYgDWQIqInSPioW0cnxsRf9+7iiRJ2+K4rV4yAEuSJKko/X4XCBUqIt4LvAn4HeB3gQuB44DfBz4CTAdOBjbQvFH2ScBUmo9PfAHw4LhrvRL4DDAErKb5dCZJUhc5bmtH4gyw+tmMzHwDcC7wIeB4mgPm+4GzgNdm5uE0H7n6QeBPgbszcw7wuXHXuQKYn5lzaT6F6dSefQeSVBbHbe0QDMDqZ3dWf44C92ZmA3iM5mMS78nMNdXxW4D9gf2A2wEy83vA09XxlwOXRsS3ac4ivLAn1UtSeRy3tUMwAKufPdtNqhvAKyLiedXrI4H7aT5L/Q8BIuLVNB8rCc2bar+7mkk4FbhxogqWpMI5bmuH4Bpg7Yg2AguBmyNiE811Yx+vjl0TEd+h+VSZDdW+D1X7h2kOwu8D9uptyZJUNMdt9RWfBCdJkqSiuARCkiRJRTEAS5IkqSgGYEmSJBXFACxJkqSiGIAlSZJUFAOwJEmSimIAliRJUlEMwJIkSSrK/wcr0nlJB7wunwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dfll['log_likelihood'].unstack().plot.bar(\n",
    "    subplots=True, layout=(1, 2), figsize=(12, 6), sharex=True);\n",
    "\n",
    "ax[0,0].set_xticks(range(5))\n",
    "ax[0,0].set_xticklabels(['k1', 'k2', 'k3', 'k4', 'k5'])\n",
    "ax[0,0].set_xlim(-.25, 4.25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ Again we're showing the linear-generated data at left (Blue) and the quadratic-generated data on the right (Green)\n",
    "+ For both datasets, as the models get more complex, the likelhood increases monotonically\n",
    "+ This is expected, since the models are more flexible and thus able to (over)fit more easily.\n",
    "+ This overfitting makes it a terrible idea to simply use the likelihood to evaluate the model fits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View posterior predictive fit\n",
    "\n",
    "Just for the linear, generated data, lets take an interactive look at the posterior predictive fit for the models k1 through k5.\n",
    "\n",
    "As indicated by the likelhood plots above, the higher-order polynomial models exhibit some quite wild swings in the function in order to (over)fit the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90023ea40004448bb211f4910de44ab8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='modelnm', options=('k1', 'k2', 'k3', 'k4', 'k5'), value='k1'), Out…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(plot_posterior_cr, models=fixed(models_lin), traces=fixed(traces_lin),\n",
    "            rawdata=fixed(dfs_lin), xlims=fixed(dfs_lin_xlims), datamodelnm=fixed('linear'),\n",
    "            modelnm = ['k1','k2','k3','k4','k5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare models using WAIC\n",
    "\n",
    "The Widely Applicable Information Criterion (WAIC) can be used to calculate the goodness-of-fit of a model using numerical techniques. See [(Watanabe 2013)](http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf) for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ We get three different measurements: \n",
    "    - waic: widely available information criterion\n",
    "    - waic_se: standard error of waic\n",
    "    - p_waic: effective number parameters\n",
    "    \n",
    "In this case we are interested in the WAIC score. We also plot error bars for the standard error of the estimated scores. This gives us a more accurate view of how much they might differ.\n",
    "\n",
    "Now loop through all the models and calculate the WAIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_trace_dict = dict()\n",
    "for nm in ['k1', 'k2', 'k3', 'k4', 'k5']:\n",
    "    models_lin[nm].name = 'poly=lin, '+nm\n",
    "    model_trace_dict.update({models_lin[nm]: traces_lin[nm]})\n",
    "\n",
    "    models_quad[nm].name = 'poly=quad, '+nm\n",
    "    model_trace_dict.update({models_quad[nm]: traces_quad[nm]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:218: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>WAIC</th>\n",
       "      <th>pWAIC</th>\n",
       "      <th>dWAIC</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>var_warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">poly=lin</th>\n",
       "      <th>k1</th>\n",
       "      <td>130.89</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k2</th>\n",
       "      <td>131.97</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k3</th>\n",
       "      <td>134.67</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k4</th>\n",
       "      <td>137.22</td>\n",
       "      <td>4.28</td>\n",
       "      <td>6.32</td>\n",
       "      <td>0</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k5</th>\n",
       "      <td>138.86</td>\n",
       "      <td>4.63</td>\n",
       "      <td>7.96</td>\n",
       "      <td>0</td>\n",
       "      <td>3.76</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">poly=quad</th>\n",
       "      <th>k4</th>\n",
       "      <td>263.74</td>\n",
       "      <td>0.92</td>\n",
       "      <td>132.85</td>\n",
       "      <td>0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k5</th>\n",
       "      <td>263.9</td>\n",
       "      <td>1.04</td>\n",
       "      <td>133.01</td>\n",
       "      <td>0</td>\n",
       "      <td>2.57</td>\n",
       "      <td>5.27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k3</th>\n",
       "      <td>265.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>134.93</td>\n",
       "      <td>0</td>\n",
       "      <td>3.33</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k2</th>\n",
       "      <td>266.57</td>\n",
       "      <td>0.64</td>\n",
       "      <td>135.67</td>\n",
       "      <td>0</td>\n",
       "      <td>3.73</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k1</th>\n",
       "      <td>267.72</td>\n",
       "      <td>0.63</td>\n",
       "      <td>136.82</td>\n",
       "      <td>0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>5.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 WAIC pWAIC   dWAIC weight    SE   dSE var_warn\n",
       "poly=lin   k1  130.89  2.22       0      1  3.98     0        1\n",
       "           k2  131.97  2.74    1.08      0  3.47  0.92        1\n",
       "           k3  134.67  3.77    3.78      0  3.71  1.08        1\n",
       "           k4  137.22  4.28    6.32      0  3.17   1.8        1\n",
       "           k5  138.86  4.63    7.96      0  3.76  2.17        1\n",
       "poly=quad  k4  263.74  0.92  132.85      0  2.73  5.21        1\n",
       "           k5   263.9  1.04  133.01      0  2.57  5.27        1\n",
       "           k3  265.83  0.85  134.93      0  3.33  5.25        1\n",
       "           k2  266.57  0.64  135.67      0  3.73   5.4        0\n",
       "           k1  267.72  0.63  136.82      0  4.01  5.53        0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfwaic = pm.compare(model_trace_dict, ic='WAIC')\n",
    "dfwaic.index = pd.MultiIndex.from_tuples(\n",
    "    [tuple(k.split(',')) for k,v in dfwaic.iterrows()])\n",
    "\n",
    "dfwaic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dfwaic['WAIC'].unstack().T.plot.line(\n",
    "    yerr=dfwaic['SE'].unstack().T,\n",
    "    subplots=True, layout=(1, 2), figsize=(12, 6), sharex=True);\n",
    "\n",
    "ax[0,0].set_xticks(range(5))\n",
    "ax[0,0].set_xticklabels(['k1', 'k2', 'k3', 'k4', 'k5'])\n",
    "ax[0,0].set_xlim(-.25, 4.25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe**\n",
    "\n",
    "+ We should prefer the model(s) with lower WAIC\n",
    "\n",
    "\n",
    "+ Linear-generated data (lhs):\n",
    "    + The WAIC seems quite flat across models\n",
    "    + The WAIC seems best (lowest) for simpler models.\n",
    "\n",
    "\n",
    "+ Quadratic-generated data (rhs):\n",
    "    + The WAIC is also quite flat across the models\n",
    "    + The lowest WAIC is model **k4**, but **k3** - **k5** are more or less the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:299: UserWarning: Estimated shape parameter of Pareto distribution is\n",
      "        greater than 0.7 for one or more samples.\n",
      "        You should consider using a more robust model, this is because\n",
      "        importance sampling is less likely to work well if the marginal\n",
      "        posterior and LOO posterior are very different. This is more likely to\n",
      "        happen with a non-robust model and highly influential observations.\n",
      "  happen with a non-robust model and highly influential observations.\"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:299: UserWarning: Estimated shape parameter of Pareto distribution is\n",
      "        greater than 0.7 for one or more samples.\n",
      "        You should consider using a more robust model, this is because\n",
      "        importance sampling is less likely to work well if the marginal\n",
      "        posterior and LOO posterior are very different. This is more likely to\n",
      "        happen with a non-robust model and highly influential observations.\n",
      "  happen with a non-robust model and highly influential observations.\"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:299: UserWarning: Estimated shape parameter of Pareto distribution is\n",
      "        greater than 0.7 for one or more samples.\n",
      "        You should consider using a more robust model, this is because\n",
      "        importance sampling is less likely to work well if the marginal\n",
      "        posterior and LOO posterior are very different. This is more likely to\n",
      "        happen with a non-robust model and highly influential observations.\n",
      "  happen with a non-robust model and highly influential observations.\"\"\")\n",
      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:299: UserWarning: Estimated shape parameter of Pareto distribution is\n",
      "        greater than 0.7 for one or more samples.\n",
      "        You should consider using a more robust model, this is because\n",
      "        importance sampling is less likely to work well if the marginal\n",
      "        posterior and LOO posterior are very different. This is more likely to\n",
      "        happen with a non-robust model and highly influential observations.\n",
      "  happen with a non-robust model and highly influential observations.\"\"\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>LOO</th>\n",
       "      <th>pLOO</th>\n",
       "      <th>dLOO</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>shape_warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">poly=lin</th>\n",
       "      <th>k1</th>\n",
       "      <td>131.14</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k2</th>\n",
       "      <td>132.38</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k3</th>\n",
       "      <td>136.02</td>\n",
       "      <td>4.44</td>\n",
       "      <td>4.88</td>\n",
       "      <td>0</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k4</th>\n",
       "      <td>139.44</td>\n",
       "      <td>5.39</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k5</th>\n",
       "      <td>141.23</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.09</td>\n",
       "      <td>0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">poly=quad</th>\n",
       "      <th>k4</th>\n",
       "      <td>263.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>132.72</td>\n",
       "      <td>0</td>\n",
       "      <td>2.78</td>\n",
       "      <td>5.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k5</th>\n",
       "      <td>264.79</td>\n",
       "      <td>1.48</td>\n",
       "      <td>133.65</td>\n",
       "      <td>0</td>\n",
       "      <td>2.84</td>\n",
       "      <td>5.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k3</th>\n",
       "      <td>265.94</td>\n",
       "      <td>0.9</td>\n",
       "      <td>134.8</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k2</th>\n",
       "      <td>266.64</td>\n",
       "      <td>0.68</td>\n",
       "      <td>135.49</td>\n",
       "      <td>0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k1</th>\n",
       "      <td>267.78</td>\n",
       "      <td>0.66</td>\n",
       "      <td>136.63</td>\n",
       "      <td>0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LOO  pLOO    dLOO weight    SE   dSE shape_warn\n",
       "poly=lin   k1  131.14  2.34       0      1  4.05     0          0\n",
       "           k2  132.38  2.94    1.24      0  3.54  0.97          0\n",
       "           k3  136.02  4.44    4.88      0  4.08  1.38          1\n",
       "           k4  139.44  5.39     8.3      0  3.62  2.52          1\n",
       "           k5  141.23  5.82   10.09      0  4.35  2.67          1\n",
       "poly=quad  k4  263.87  0.98  132.72      0  2.78  5.29          0\n",
       "           k5  264.79  1.48  133.65      0  2.84  5.35          1\n",
       "           k3  265.94   0.9   134.8      0   3.4  5.34          0\n",
       "           k2  266.64  0.68  135.49      0  3.78  5.49          0\n",
       "           k1  267.78  0.66  136.63      0  4.06  5.62          0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfloo = pm.compare(model_trace_dict, ic='LOO')\n",
    "dfloo.index = pd.MultiIndex.from_tuples(\n",
    "    [tuple(k.split(',')) for k,v in dfloo.iterrows()])\n",
    "\n",
    "dfloo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dfloo['LOO'].unstack().T.plot.line(\n",
    "    yerr=dfloo['SE'].unstack().T,\n",
    "    subplots=True, layout=(1, 2), figsize=(12, 6), sharex=True);\n",
    "\n",
    "ax[0,0].set_xticks(range(5))\n",
    "ax[0,0].set_xticklabels(['k1', 'k2', 'k3', 'k4', 'k5'])\n",
    "ax[0,0].set_xlim(-.25, 4.25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare leave-one-out Cross-Validation [LOO]\n",
    "\n",
    "Leave-One-Out Cross-Validation or K-fold Cross-Validation is another quite universal approach for model selection. However, to implement K-fold cross-validation we need to paritition the data repeatly and fit the model on every partition. It can be very time consumming (computation time increase roughly as a factor of K). Here we are applying the numerical approach using the posterier trace as suggested in Vehtari et al 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe**\n",
    "\n",
    "+ We should prefer the model(s) with lower LOO. You can see that LOO is nearly identical with WAIC. That's because WAIC is asymptotically equal to LOO. However, PSIS-LOO is supposedly more robust than WAIC in the finite case (under weak priors or influential observation). \n",
    "\n",
    "\n",
    "+ Linear-generated data (lhs):\n",
    "    + The LOO is also quite flat across models\n",
    "    + The LOO is also seems best (lowest) for simpler models.\n",
    "\n",
    "\n",
    "+ Quadratic-generated data (rhs):\n",
    "    + The same pattern as the WAIC\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final remarks and tips\n",
    "\n",
    "It is important to keep in mind that, with more data points, the real underlying model (one that we used to generate the data) should outperform other models. \n",
    "\n",
    "There is some agreement that PSIS-LOO offers the best indication of a model's quality. To quote from [avehtari's comment](https://github.com/pymc-devs/pymc3/issues/938#issuecomment-313425552): \"I also recommend using PSIS-LOO instead of WAIC, because it's more reliable and has better diagnostics as discussed in http://link.springer.com/article/10.1007/s11222-016-9696-4 (preprint https://arxiv.org/abs/1507.04544), but if you insist to have one information criterion then leave WAIC\". \n",
    "\n",
    "Alternatively, Watanabe [says](http://watanabe-www.math.dis.titech.ac.jp/users/swatanab/index.html) \"WAIC is a better approximator of the generalization error than the pareto smoothing importance sampling cross validation. The Pareto smoothing cross validation may be the better approximator of the cross validation than WAIC, however, it is not of the generalization error\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "\n",
    "For more information on Model Selection in PyMC3, and about Bayesian model selection, you could start with:\n",
    "\n",
    "+ Thomas Wiecki's [detailed response](https://stats.stackexchange.com/questions/161082/bayesian-model-selection-in-pymc3/166383#166383) to a question on Cross Validated\n",
    "+ The Deviance Information Criterion: 12 Years On [(Speigelhalter et al 2014)](http://onlinelibrary.wiley.com/doi/10.1111/rssb.12062/abstract)\n",
    "+ Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models [(Ando 2007)](https://doi.org/10.1093/biomet/asm017)\n",
    "+ A Widely Applicable Bayesian Information Criterion [(Watanabe 2013)](http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf)\n",
    "+ Efficient Implementation of Leave-One-Out Cross-Validation and WAIC for Evaluating Fitted Bayesian Models [(Vehtari et al 2015)](http://arxiv.org/abs/1507.04544)"
   ]
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